AI Frameworks · vibrantlabsai

ragas

Ragas is a Python framework for evaluating and optimizing Large Language Model applications through objective metrics, automated test generation, and production-aligned feedback loops. It provides pre-built evaluation metrics tailored for LLM and RAG systems, integrates with popular frameworks like LangChain, and helps teams move from subjective assessments to data-driven evaluation workflows.

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Source: GitHub — github.com/vibrantlabsai/ragas

14.7k

GitHub stars

1.5k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	vibrantlabsai/ragas
Owner	vibrantlabsai
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	14.7k
Forks	1.5k
Open issues	478
Latest release	v0.4.3 (2026-01-13)
Last updated	2026-02-24
Source	https://github.com/vibrantlabsai/ragas

What ragas is

Ragas is an Apache-2.0 licensed Python library that implements LLM-based and traditional metrics for evaluating LLM outputs. It supports async/await patterns, integrates with major LLM clients (OpenAI, etc.), offers templated quickstart projects for RAG evaluation, and includes instrumentation for production data feedback loops.

Quickstart

Get the ragas source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/vibrantlabsai/ragas.git2cd ragas3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

RAG System Evaluation

Evaluate retrieval augmented generation systems with metrics designed for relevance, faithfulness, and answer quality. Use the built-in rag_eval template to bootstrap testing immediately.

LLM Output Quality Assessment

Score LLM responses against custom evaluation criteria using discrete metrics and aspect critiques. Supports custom prompts and evaluation rubrics without requiring labeled training data.

Automated Test Dataset Generation

Generate production-aligned test datasets automatically from your knowledge base or documents. Reduces manual effort in creating comprehensive evaluation suites covering diverse scenarios.

Implementation considerations

Initialize LLM client (e.g., AsyncOpenAI) and configure API credentials; environment variable setup required for authentication.
Select appropriate metrics for your use case (DiscreteMetric, Aspect Critique, etc.) and customize evaluation prompts if needed.
Plan async execution model; Ragas uses asyncio extensively, requiring async-compatible application architecture.
Cost estimation: budget for LLM API calls; metric scoring cost depends on model selection and evaluation frequency.
Test with a small dataset first; validate metric behavior and prompt wording before scaling to production evaluation runs.

When to avoid it — and what to weigh

Real-time Latency-Critical Systems — Ragas evaluation is LLM-based and async, introducing network latency. Not suitable for systems requiring sub-100ms evaluation response times.
Offline-Only or Air-Gapped Deployments — Requires external LLM API calls (OpenAI, etc.) for scoring. Cannot run purely offline without custom LLM integration or local model setup.
Non-Python Technical Stacks — Pure Python library with no native Go, Java, or Node.js SDKs. Would require wrapping or API gateway for non-Python applications.
Low-Cost Evaluation at Scale — LLM-based metrics incur per-call costs. High-volume evaluation could accumulate significant API charges depending on metric selection.

License & commercial use

Apache License 2.0 is a permissive OSI-approved license allowing commercial use, modification, and distribution.

Apache-2.0 permits commercial use, including in proprietary products. Retain license and copyright notices in derivatives. No explicit warranty; review LICENSE file for indemnification limits before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No data provided on security audits, vulnerability disclosure process, or threat model. LLM-based evaluation sends data to external APIs; ensure API keys and evaluation payloads comply with data governance policies. Anonymized analytics enabled by default; review _analytics.py code if data residency is critical. No security claims inferred without supporting evidence.

Alternatives to consider

LlamaIndex (formerly Gpt-Index)

Offers evaluation modules alongside RAG orchestration; integrated approach if building end-to-end RAG systems. Requires more framework coupling.

DeepEval

Comparable LLM-based evaluation library; may offer different metric sets or integrations. Direct competitor in evaluation-focused tooling.

Custom Evaluation Scripts with LLM APIs

Lower-level approach using OpenAI/Anthropic directly; full control but higher development overhead and no pre-built metrics or templates.

Software development agency

Build on ragas with DEV.co software developers

Ragas offers a low-barrier entry point with quickstart templates and active community support. Start with the rag_eval template, test with your data, and scale evaluation workflows systematically.

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ragas FAQ

Can I use Ragas without paying for LLM API calls?

Not directly. Ragas is LLM-based; metrics rely on external model inference. You could extend it with local model wrappers (e.g., Ollama), but this is not documented in the provided data and requires custom integration.

Is Ragas production-ready?

Active maintenance, significant community adoption (14.7k stars), and v0.4.3 release suggest maturity. However, 478 open issues indicate ongoing refinement. Test thoroughly in your environment before critical deployments.

Does Ragas work with non-OpenAI LLMs?

Yes, llm_factory supports multiple LLM providers. Specific vendor support list not provided in data; check documentation for your LLM choice.

What is the 'RAGAS_DO_NOT_TRACK' environment variable for?

Ragas collects minimal anonymized usage data by default to improve the product. Set RAGAS_DO_NOT_TRACK=true to opt out. Analytics code is open-source and aggregated data is published.

Work with a software development agency

Adopting ragas is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.

Ready to improve LLM evaluation?

Ragas offers a low-barrier entry point with quickstart templates and active community support. Start with the rag_eval template, test with your data, and scale evaluation workflows systematically.

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